Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

Abstract

This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation. Firstly, a Depth Sorting Fast Search (DSFS) algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees∗ (Q-RRT∗) algorithm. A cost inequality relationship between an ancestor and its descendants was derived, and the ancestors were filtered accordingly. Secondly, the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm, taking into account the fitness, safety, and asymptotic optimality of the routes, according to the gravity suitability distribution of the navigation space. Finally, experimental comparisons of the computing performance of the ChooseParent procedure, the Rewire procedure, and the combination of the two procedures for Q-RRT∗ and DSFS were conducted under the same planning environment and parameter conditions, respectively. The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT∗ algorithm while ensuring correct computational results.